Abstract
Automatic air brake systems are widely used in freight train braking to ensure railway operation safety. Various types faults pose an enormous threat to freight operations. Existing algorithms lack a unified framework for generating key features. In this research, we propose a novel feature engineering framework for the fault diagnosis of freight train air brakes. First, experimental data are collected through a three-car in-lab experimental platform. Second, a peak detection method combined with first-order difference function to partition and classify the air pressure time series into the braking phase and releasing phase. Third, a divided-and-integrated framework is designed for feature engineering. Feature selection is carried out via a modified reinforcement learning method. Finally, multiple machine learning algorithms are explored and the results indicate that random forest method shows the best performance. The proposed model achieves about 99% accuracy for car-level fault detection and over 94% accuracy for component-level fault diagnosis.
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